WIPO Translate: Terms and Conditions for the Usage andUser Guide
https://patentscope.wipo.int/translate

A. What is it?
WIPO Translate is an AI-powered, instant patent translation tool, developed by WIPO for 18
languages pairs. It is particularly powerful for translation of sentences and phrases oftechnical nature, since WIPO Translate was trained, and continues to be updated, by big data
generated by published patent documents.
This tool is designed to give users the possibility to understand the content of a text andprovide alternative translations for each segment of the text. Since September 2016, NeuralMachine Translation is used to provide more accurate translation of patent texts, WIPO
Translate (since September 2017) offers Neural Machine Translation in all 10 official
languages of the PCT, the tool is powered by the open source “Marian NMT”1.
The interface proposes:

1

https://marian-nmt.github.io/

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For example, you can enter the following sentence in the interface, click on “translate”, andthe source text and the translation will be displayed:

Hovering the mouse on the left highlights
corresponding segment on the right (andvice-versa)

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p. 2/8By clicking on a segment other proposals will be displayed. Selecting one of them willautomatically change the output text.

Note that, when the text is long, parallel segments are highlighted in red, which helps
identify which part of the source text is translated.
Clicking on the source or target text, allows the user to enter his own translation of thesegment:

By clicking on a segment, thetool displays additional
proposals

User can even enterhis own text

User can select an existing
proposal, it will be automaticallyreplaced in the text

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p. 3/8When the user wants more proposals for a particular phrase, he can select the source phrase,
the phrase will be segmented from the rest of the sentence and more proposals will bedisplayed.
E.g. if the user wants to look for more proposals for “torque pin”, he first selects the phrase,
waits a second and gets the following display:

This functionality can also be used to further segment long sentences. In the example, thelast sentence is too long for the tool to display good proposals, therefore we can select aphrase in the middle of the sentence to further split the sentence (e.g. by selecting “fortransmitting torque”):

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p. 4/8When the translation is finished, do not try to copy and paste it from the right box straightaway, you first have to click on “Edit translation” (this is due to a technical behavior thatwould otherwise copy all the alternative translations).
You will then be able to further edit the translation before copying it (Note that the “postedited” translations are not currently used on WIPO Translate to retrain the system).

B. How to use it?
When you first enter a text, without further input and click on “translate“ the tool will try toguess the language pair from the text (note that it is not always accurate!) and try to guess thedomain it belongs to.
How to set and use domains?
The system is trained on patents belonging to specific domains. It can use this information toresolve some translation ambiguities (e.g. the English word “rope” can be translated as“corde” – a textile rope - or “cable” – a metal rope- in French). You can specify the domain
by yourself or let the tool guess it from the content. In any case, if you cannot decide between
several domains, you can always change it afterwards and look at how the translations differ.

C. How does it work?
The tool has been trained using a corpus of patent title and abstract pairs translated by humans.
It has also been trained on aligned descriptions and claims. It is based on Machine Learningtechniques, which means it tries to reproduce phrase translations it has already seen during thetraining.
Therefore, the tool is specific to patent texts. It will not be accurate when translating any otherkind of text (news, legal texts, conversations, etc.). The typical example is “I am”, which has
never been seen in training data, therefore will be badly translated in any languages.

D. Short history
In the past (since 2009), the tool was using Phrase-Based Statistical Machine Translation(using the open-source “Moses”2), in 2016 WIPO experimented with Neural MachineTranslation (“Nematus3 /Amunmt”). Since September 2017, all models are based on Neural
Machine Translation, using the open source Marian4.

E. Who can use it?
Anyone can use it for free of charge. However, robots are not allowed to use WIPO Translate.
To avoid robotic use, WIPO Translate requests you to register yourself if frequent uses aredetected. You may be asked to enter a “captcha” (enter a text displayed on an image) toensure that any user or program (robot) is not overloading the system. Do not use the toolwith any automatic program to translate batch of texts, it will overload the system. An antirobot system has been set up and any abuse will end-up in a black list which blocks any more
use in future.

2

http://www.statmt.org/moses/
https://github.com/EdinburghNLP/nematus
4
https://marian-nmt.github.io/
3

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F. Conditions for the Usage
WIPO forbids the submission of text than more than 2,000 characters (characters in the senseof signs, “The D.N.A.” counts for 10 characters. Binary codes like Chinese/Japanesecharacters count for one, “電気バイク”counts for 5 characters).

G. Security and Privacy
WIPO Translate is serviced through https in a industry standard secured protocol. Your
request for translation and the transmission of the translated text are made secured by thestandard encrypted communication protocol.
WIPO neither publish nor provide to any party log data and identity of users of WIPO
Translate. For further information, please refer to WIPO’s Privacy Policy, which isincorporated herein by this reference.

H. Disclaimers
WIPO Translate does not ensure the quality of translation matched with human translation.
Be careful when using the tool: it should be used for information purposes only. The
translation results may contain discrepancies or mistakes. WIPO has no liability of thequality of services provided by WIPO Translate.
While WIPO makes every effort to ensure that WIPO Translate is free of any software virus,
it cannot guarantee that the material is free from any or all software viruses. WIPO is not
responsible for any loss or damage howsoever caused by the software and related codes,including viruses and worms.

I. Privileges and immunities
Nothing in or relating to these Terms and Conditions shall be deemed or interpreted as awaiver of any of the privileges and immunities accorded to WIPO as internationalorganization and specialized agency of the United Nations.

J. Dispute Settlement
Any dispute between WIPO and users arising out of or relating to these Terms and Conditions
that cannot be resolved amicably shall be referred to arbitration in accordance with theUNCITRAL Arbitration Rules then in force. The parties shall be bound by any arbitration
award rendered as a result of such arbitration as the final adjudication of such a dispute.

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K. Bibliography
The system has been described in the following publications (chronological order):(2017) WIPO Translate: Patent Neural Machine Translation publicly available in 10 languages. Invited talk inPSLT 2017 workshop, Nagoya, September 2017.
https://www.researchgate.net/publication/320058012_WIPO_Translate_Patent_Neural_Machine_Translation_publicly_available_in_10_languages(2016) Practical use of machine translation in international organization. Invited lecture in Icon2016 conference,
Varanasi, India, 19-20/12/2016. https://lnkd.in/f9jTmBJ(2015) Full-text Patent translation at WIPO: scalability, quality and usability, Workshop on Patent and Scientific
Literature Translation (PSLT 2015), Miami, October 2015.
https://www.researchgate.net/publication/283508042_Fulltext_Patent_translation_at_WIPO_scalability_quality_and_usability(2015) WIPO Translate: overcoming language barriers in patent information, The European IPR helpdesk,
Bulletin no 17, April-July 2015, pp. 10-11,
https://www.iprhelpdesk.eu/sites/default/files/newsdocuments/IPR_Bulletin_No17_1.pdf(2014) Bruno Pouliquen: Patent Machine Translation (handling large data with Moses), keynote presentation at
Machine Translation Marathon, 8-13/09/2014, Trento, Italy, [PDF presentation 39 slides, 1.2M]
http://statmt.org/mtm14/uploads/Main/PatentMT_MTM2014.pdf(2014) Marcin Junczys-Dowmunt and Bruno Pouliquen: SMT of German Patents at WIPO: Decompounding andVerb Structure Pre-reordering. In 17th Annual Conference of the European Association for MachineTranslation (EAMT), 16-18 June 2014, Dubrovnik, Croatia, [PDF]
https://www.researchgate.net/publication/263099943_SMT_of_German_Patents_at_WIPO_Decompounding_and_Verb_Structure_Prereordering(2013) Bruno Pouliquen, Christophe Mazenc & Paul Halfpenny: Latest developments in machine translation atWIPO, EPO- East meets West, 18-19 April 2013, Vienna, Austria, [PDF presentation 14 slides, 590Kb]
http://documents.epo.org/projects/babylon/eponot.nsf/0/27A72BD08630377DC1257B5F00434488/$File/9_wipo_pouliquen.pdf(2011) Bruno Pouliquen & Christophe Mazenc: Automatic translation tools at WIPO. Aslib, Translating and theComputer 33, 17-18 Nov 2011, London; 6pp. [PDF, 407Kb] http://www.mt-archive.info/Aslib-2011-Pouliquen.pdf(2011) Bruno Pouliquen & Christophe Mazenc: COPPA, CLIR and TAPTA: three tools to assist in overcoming
the patent barrier at WIPO. MT Summit XIII: the Thirteenth Machine Translation Summit [organized by the]Asia-Pacific Association for Machine Translation (AAMT), 19-23 September 2011, Xiamen, China; pp.2430. [PDF, 896KB] http://www.mt-archive.info/MTS-2011-Pouliquen.pdf(2011) Bruno Pouliquen, Christophe Mazenc & Aldo Iorio: Tapta: a user-driven translation system for patentdocuments based on domain-aware statistical machine translation. [EAMT 2011]: proceedings of the 15th
conference of the European Association for Machine Translation, 30-31 May 2011, Leuven, Belgium; eds.
Mikel L.Forcada, Heidi Depraetere, Vincent Vandeghinste; pp.5-12. [PDF, 342KB]; presentation, 15 slides[PDF, 1642KB]. http://www.mt-archive.info/EAMT-2011-Pouliquen.pdf

Tapta4UN (the same tool tailored for United Nations) has been described in thefollowing publications:(2013) Bruno Pouliquen, Cecilia Elizalde, Marcin Junczys-Dowmunt, Christophe Mazenc & Jose GarciaVerdugo: Large-scale multiple language translation accelerator at the United Nations. [MT Summit 2013],
Proceedings of the XIV Machine Translation Summit, Nice September 2-6, pp. 345-352 [PDF, 572KB]
http://www.mtsummit2013.info/files/proceedings/main/mt-summit-2013-pouliquen-et-al.pdf(2012) Cecilia Elizalde, Bruno Pouliquen, Christophe Mazenc & José García-Verdugo: TAPTA4UN:
collaboration on machine translation between the World Intellectual Property Organization and the UnitedNations. Aslib 2012 - Translating and the Computer 34, 29-30 November 2012, London, UK; [PDF, 168KB],
http://www.mt-archive.info/Aslib-2012-Elizalde.pdf(2012) Bruno Pouliquen, Christophe Mazenc, Cecilia Elizalde, & Jose Garcia-Verdugo: Statistical machinetranslation prototype using UN parallel documents. EAMT 2012: Proceedings of the 16th Annual Conference ofthe European Association for Machine Translation, Trento, Italy, May 28-30 2012, ed. Mauro Cettolo, MarcelloWIPO Translate, terms and conditions for the usage and user manual(last update: December 15, 2020)
p. 7/8Federico, Lucia Specia, Andy Way; pp.12-19, [PDF, 251KB], http://www.mt-archive.info/EAMT-2012Pouliquen.pdf

The same software has been installed in other UN agencies (ITU, IMO, FAO etc.):(2015) Bruno Pouliquen: SMT in various United Nations agencies, Machine Translation Summit, Miami,
October 2015; https://www.researchgate.net/publication/283508669_SMT_in_various_United_Nations_agencies(2015) Bruno Pouliquen, Marcin Junczys-Dowmunt, Blanca Pinero, Michał Ziemski, SMT at the International
Maritime Organization: Experiences with Combining In-house Corpora with Out-of-domain Corpora.
Proceedings of EAMT 2015 conference, Antalya, Turkey, May 2015;
https://www.researchgate.net/publication/276885856_SMT_at_the_International_Maritime_Organization_Exper
iences_with_Combining_In-house_Corpora_with_Out-of-domain_Corpora

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